Abstract:This paper is a technical presentation
of Artificial Linguistic Internet Computer Entity (A.L.I.C.E.) and Artificial
Intelligence Markup Language (AIML), set in context by historical and
philosophical ruminations on human consciousness. A.L.I.C.E., the first
AIML-based personality program, won the Loebner Prize as "the most human
computer" at the annual Turing Test contests in 2000 and 2001. (Loebner 2002) The
program, and the organization that develops it, is a product of the world of
free software. More than 500 volunteers from around the world have contributed
to her development. This paper describes the history of A.L.I.C.E. and
AIML-free software since 1995, noting that the theme and strategy of deception
and pretense upon which AIML is based can be traced through the history of
artificial intelligence research. This paper goes on to show how to use AIML to
create robot personalities like A.L.I.C.E. that pretend to be intelligent and
self-aware. The bot ‘personality' is a set of AIML files consisting of simple
stimulus-response modules called categories. Each <category> contains a
<pattern>, or "stimulus," and a <template>, or "response." AIML
software stores the stimulus-response categories in a tree managed by an object
called the Graphmaster. When a bot client inputs text as a stimulus, the
Graphmaster searches the categories for a matching <pattern>, along with
any associated context, and then outputs the associated <template> as a
response. These categories can be structured to produce more complex humanlike
responses with the use of a very few markup tags. AIML bots make extensive use
of the multi-purpose recursive <srai> tag, as well as two AIML context
tags, <that> and <topic>. Conditional branching in AIML is
implemented with the <condition> tag. AIML implements the ELIZA personal
pronoun swapping method with the <person> tag. Bot personalities are
created and shaped through a cyclical process of supervised learning called
Targeting. Targeting is a cycle incorporating client, bot, and botmaster,
wherein client inputs that find no complete match among the categories are
logged by the bot and delivered as Targets the botmaster, who then creates
suitable responses, starting with the most common queries. The Targeting cycle
produces a progressively more refined bot personality. The art of AIML writing
is most apparent in creating default categories, which provide noncommittal
replies to a wide range of inputs. The paper winds up with a survey of some of
the philosophical literature on the question of consciousness. We consider
Searle's Chinese Room, and the view that natural language understanding by a
computer is impossible. We note that the proposition "consciousness is an
illusion" may be undermined by the paradoxes it apparently implies. We conclude
that A.L.I.C.E. does pass the Turing Test, at least, to paraphrase Abraham
Lincoln, for some of the people some of the time.

TABLE OF CONTENTS

1. Introduction

2. The Problem

3. The Psychiatrist

4. Politicians

5. Parties

6. The Professor

7. PNAMBIC

8. The Prize

9. The Portal

10. Penguins

11. Programs

12. Categories

13. Recursion

14. Context

15. Predicates

16. Person

17. Graphmaster

18. Matching

19. Targeting

20. Defaults

21. Philosophers

22. Pretending

23. Consciousness

24. Paradox

25. Conclusion

ACKNOWLEDGEMENTS

REFERENCES

1.Introduction

A.L.I.C.E. is an
artificial intelligence natural language chat robot based on an experiment
specified by Alan M. Turing in 1950. The A.L.I.C.E. software utilizes AIML, an
XML language we designed for creating stimulus-response chat robots.

Some view A.L.I.C.E.
and AIML as a simple extension of the old ELIZA psychiatrist program. The
comparison is fair regarding the stimulus-response architecture. But the
A.L.I.C.E. bot has at present more than 40,000 categories of knowledge, whereas
the original ELIZA had only about 200. Another innovation was provided by the
web, which enabled natural language sample data collection possible on an
unprecedented scale.

A.L.I.C.E. won the
Loebner Prize, an annual Turing Test, in 2000 and 2001. Although no computer
has ever ranked higher than the humans in the contest she was ranked "most
human computer" by the two panels of judges. What it means to"Pass the Turing
Test" is not so obvious. Factors such as the age, intellect and expectations of
the judges have tremendous impact on their perceptions of intelligence. Alan
Turing himself did not describe only one"Turing Test." His original imitation game
involved determining the gender of the players, not their relative humanness.

The model of learning
in A.L.I.C.E. is called supervised learning because a person, the botmaster,
plays a crucial role. The botmaster monitors the robot's conversations and creates
new AIML content to make the responses more appropriate, accurate, believable,
or"human," or whatever the botmaster intends. We have developed algorithms for
automatic detection of patterns in the dialog data. This process, called
"Targeting," provides the botmaster with new input patterns that do not already
have specific replies, permitting a process of almost continuous supervised
refinement of the bot.

Some have argued that
Turing, when he predicted that a machine could play his game in"50 years"
after his 1950 paper, envisioned something more like a general purpose learning
machine, which does not yet exist. The concept is simple enough: build a robot
to grow like a child, able to be taught language the way we are. In our terms,
the role of the botmaster would be fully automated. But even a child does not,
or at least should not, go forth into the world, unprotected, to learn language
"on the street," without supervision.

Automatic generation
of chat robot questions and answers
appears likely to raise the same trust issues forced upon the abandoned child.
People are simply too untrustworthy in the"facts" that they would teach the
learning machine. Many clients try to deliberately sabotage the bot with false
information. There would still have to be an editor, a supervisor, a botmaster
or teacher to cull the wheat from the chaff.

The brain of
A.L.I.C.E. consists of roughly 41,000 elements called categories. Each category
combines a question and answer, or stimulus and response, called the"pattern"
and"template" respectively. The AIML software stores the patterns in a tree
structure managed by an object called the Graphmaster, implementing a pattern
storage and matching algorithm. The Graphmaster is compact in memory, and
permits efficient pattern matching time.

2.The Problem

Susan Sterrett's
careful reading of Turing's 1950 paper reveals a significant distinction
between two different versions of what has come to be known as the Turing Test
(Sterrett 2000). The first version, dubbed the Original Imitation Game (OIG),
appears on the very first page of Computing
Machinery and Intelligence (Turing 1950). The OIG has three players: a man
(A), a woman (B), and a third person (C) of either sex. The third player (C) is
called the interrogator, and his function is to communicate with the other two,
through what would nowadays be called a text-only instant messaging chat
interface, using two terminals (or today perhaps, two windows) labeled (X) and
(Y). The interrogator must decide whether (X) is (A) and (Y) is (B), or (X) is
(B) and (Y) is (A), in other words which is the man and which is the woman. The
interrogator's task is complicated by the man (A), who Turing says should reply
to the interrogator with lies and deceptions. For example, if the man is asked,
"are you a man or a woman?," he might reply,"I am a woman."

Putting aside the
gender and social issues raised by the OIG, consider the OIG as an actual
scientific experiment. Turing's point is that if we were to actually conduct
the OIG with a sufficiently large sample of subjects playing the parts of (A),
(B), and (C), then we could measure a specific percentage M of the time that,
on average, the interrogator misidentifies the woman, so that 100-M% of the
time she is identified correctly. Given enough trials of the OIG, at least in a
given historical and cultural context, the number M ought to be a fairly
repeatable measurement.

Now, as Turing said,
consider replacing the man (A) with a computer. What would happen if we tried
the experiment with a very simple minded program like ELIZA? In that case, the
interrogator (C) would identify the woman correctly (nearly) 100 percent of the
time, so that M=0. The ELIZA program would not do well in the OIG, but as the
variety and quality of machine's responses begin to approach those of the lying
man, the measured percentage of incorrect identification ought to be closer and
closer to the M measured with the man playing (A).

Much later in the 1950
paper, in section 5, Turing describes a second game more like the concept of a
"Turing Test" as most engineering schools teach it. The setup is similar to the
OIG, but now gender plays no role. The player (B) is called"a man" and the
player (A) is always a computer. The interrogator must still decide whether (X)
is (A) and (Y) is (B), or (X) is (B) and (Y) is (A), in other words which is
the man and which is the machine. Sterrett calls this second game the Standard
Turing Test (STT).

Whole academic
conferences have been devoted to answering the question of what Turing meant by
the Turing Test. In a radio interview taped by the BBC, Turing describes a game
more like the STT, but in the paper he gives more prominence to the OIG. Unlike
the OIG, the STT is not a good scientific experiment. What does it mean to
"pass" the STT? Must the interrogator identify the machine correctly 50% of the
time, or 100%? For how long must the machine deceive the interrogator? Finally,
does the interrogator know in advance that he is trying to"out"(Zdenek 2000)
the robot, or that one of the players is a machine at all?

Unfortunately the STT,
though flawed as an experiment, has come to be popularized as the modern
"Turing Test." The STT is the basis of real-world Turing Tests including the
Loebner Prize, won by A.L.I.C.E. in 2000 and 2001. Although she performs well
in STT style contests, the A.L.I.C.E. personality is actually designed to play
the OIG. She is a machine, pretending to be a man, pretending to be a woman.
Her technology is based on the simplest A.I. program of all, the old ELIZA psychiatrist.

3.The Psychiatrist

The story of Joseph
Weizenbaum is in many ways almost as interesting as that of Alan Turing,
although not so tragic. One almost pictures Weizenbaum as a latter-day Luddite,
smashing artificial intelligence the way British tradesmen smashed factories in
reaction to the Industrial Revolution. But unlike the Luddites, Weizenbaum
himself was the inventor of the machine. It would be more akin to the Wright
Brothers destroying their prototype airplane after its first flight, saying"this
technology is too dangerous. We must put the genie back in the bottle."

A typical conversation
with ELIZA is the following:

C: Well, my boyfriend
made me come here.

R: Your boyfriend made
you come here.

C: I am unhappy.

R: Do you think coming
here will help you not to be unhappy?

C: I need some help,
that much seems certain.

R: What would it mean
to you if you got some help?

C: Perhaps I could
learn to get along with my mother.

R: Tell me more about
your family.

C: You are like my
father in some ways.

R: What resemblance do
you see?

C: My father is afraid
of everybody.

R: What else comes to
mind when you think of your father.

C: Bullies.

R: Does that have
anything to with the fact that your boyfriend

made you come here?

Note: In all sample
dialogues following, the"R:" stands for

"Robot:" and the"C:"
stands for"Client."

One of ELIZA's
fundamental"tricks" is the pronoun reversal exhibited in the first exchange.
Mapping"my" to"your" and"me" to"you," the robot echoes the client input by
exchanging first and second personal pronouns. Other replies are generic
defaults, giving the illusion of some understanding. If the client says
anything with the prefix"I need some X," the robot replies with"What would it
mean to you if you got some X?" It doesn't matter whether X is help, money,
food, water, love or time. The same answer will cover almost all the likely
inputs.

Still other ELIZA
replies are based on simple keyword recognition, as in the exchange about the
client's mother, when the robot says,"Tell me more about your family." The
appearance of the keyword"mother" anywhere in the input may have triggered
this response. ELIZA has a limited memory of the conversation state, as well.
When confronted with the unrecognized input"Bullies," she responds by raising
the previously stored topic.

As unlikely as it
sounds today, Weizebaum pulled the plug on ELIZA (Weizenbaum 1976). He was
horrified that anyone would actually believe this simple program said anything
about intelligence, let alone had any. Weizenbaum tells us that he was shocked
by the experience of releasing ELIZA, also known as"Doctor," for use by
nontechnical staff at MIT. Secretaries and nontechnical staff thought the
machine was a"real" therapist, and spent hours revealing their personal
problems to the program. When Weizenbaum informed a secretary that, of course,
he had access the logs of all the conversations, she reacted with outrage at
this invasion of privacy. Weizenbaum was shocked that such a simple program could
deceive a naive client into revealing personal information.

What Weizenbaum found
especially revolting was that the Doctor's patients believed the robot really
understood their problems. Even some psychiatrists seriously believed the robot
therapist could help patients in a constructive way. Weizenbaum's reaction
might be best understood like that of a Western physician's disapproval of
herbal medicine, or an astronomer's disdain for astrology.

The back cover of the
paper edition of Weizenbaum's Computer Power and Human Reason (Weizenbaum,
1976) gives us a feeling for the general attitude toward the book at the time
of its release:

"
Dare I say it? This is the best book I
have read on the impact of computers on society, and on technology, and man's
image of himself."—Keith Oakley, Psychology
Today

"A thoughtful blend of insight,
experience, anecdote, and passion that will stand for a long time as the
definitive integration of technological and human thought."—American
Mathematical Monthly

"Superb ... the work
of a man who is struggling with the utmost seriousness to save our humanity
from the reductionist onslaught of one of the most prestigious, and richly
funded technologies of our time."—Theodore Piszak, The Nation.

Computer Power and Human Reason seems a bit quaint today, much the same as
Turing's 1950 paper does. For one thing, Weizenbaum perceived his mission as
partly to educate an uninformed public about computers. Presumably the
uninformed public was confusing science fiction with reality. Most of the book
is devoted to explaining how a computer works, perhaps a necessary primer in
1976, but today more appropriate in a book called something like,"Computers
for Dummies."

Two chapters of Computer Power and Human Reason are
devoted to a humanist attack on artificial intelligence, on ELIZA specifically,
and on computer science research in general.

Most contemporary
scholars did not need much convincing that ELIZA was at best a gimmick, at
worst a hoax, and in any case not a"serious" artificial intelligence project.
Yet ELIZA is perhaps the most widely distributed program in the history of
artificial intelligence. The irony of Joseph Weizenbaum is that by failing to
promote his own technology, indeed by encouraging his own critics, he
successfully blocked much further investigation into what would prove to be one
of the most persistently interesting demonstrations to emerge from the MIT AI
Lab.

4.Politicians

Client: Do you think President Clinton
should be impeached?

Robot: It depends what you mean by
"thinking."

Politicians, at least those in our society, never
seem to give a straight answer to a question. If a journalist asks a specific
question, the politician answers with a"sound bite" or short, memorized speech
which is related to, but does not necessarily answer, the reporter's question.
If asked about schools for example, the politician may reply with a memorized
policy statement on education policy. It is as though the response was merely
triggered by the keyword"school" in the question. No creative or spontaneous
thinking is required.

One aspect of the art
of writing AIML is composing brief, concise, interesting, grammatically correct
and sometimes humorous default responses, which work in for a wide variety of
inputs matching a single pattern. President Clinton inadvertently revealed this
type of automatic reply when he uttered the famous quote,"It depends on what
the meaning of ‘is' is." This could be a default response to any input
beginning with the word"is." Like the politician's replies, the default
responses should indicate an understanding of the question, but not offend
anyone.

There is a democratic
aspect to A.L.I.C.E. Born out of frustration about public apathy toward the
existing attempts at artificial intelligence, A.L.I.C.E. is designed to answer
the question, what do people expect an A.I. ought to be able to say? It turns
out that there is a Zipf like distribution of the queries people make to the
robot. The appearance of the Zipf distribution leads to a natural strategy for working
on the problem: write replies for the most common queries first.

Despite the obvious
simplicity of this approach, largely borrowed from the ELIZA psychiatrist
program, the prevailing prejudice for many years, at least among the academic
elite, has derided ELIZA as a"toy," too simple to form the basis of a
practical natural language understanding system. One school of thought
advocated an approach based on limited domains of discourse, in which practical
systems might converse about one subject like moon rocks, blocks, or Star Trek.
This school would have us lower our expectations for general natural language
understanding however. Another school favors development of large scale
ontologies of"commonsense knowledge," thought to be necessary for understanding
everyday language. Still a third school advocates building something like the
general learning machine, which can be taught language like a child. It goes
without saying that no other school has produced any machine capable of
anything close to the performance of A.L.I.C.E. in Turing's Imitation Game.

5.Parties

Social situations
prove awkward for many of us. The author, challenged by an almost total apathy
toward organized sports of any kind, navigated the minefield of adolescence in
America by making use of a technique he called"sports talk." When cornered
with the question,"Did you see the game last night?," the author would always
reply,"No, who won?" Many sports questions, such as"Who do you want to win
the super bowl?" can be deflected with dry humor like,"the team that scores
the most points." When confronted with a question about a specific sports
player, like"What do you think of Joe Montana this season?," can be handled
believably with imaginary biographical data, as in,"I heard his brother was a
boxer." By remembering the previous utterance, the technique may even be
extended to more involved dialogues:

C: You should have
seen the game last night.

R: What game?

C: Basketball.

R: Sorry I missed it.

Conversations with
strangers may be initiated with appropriately selected"pickup lines." Early in
the history of A.L.I.C.E. the bot also initiated conversations by randomly
selecting one of these lines. It was then possible to measure the average
dialogue length of conversations initiated with each line. Although the
original data was sadly lost, the experiment may be easily repeated with the
latest generation of AIML software. In our brief test, the pickup line with the
longest average dialogue length was,"What is your favorite movie?" Among about
40 pickup lines tested, the lowest ranked was,"What do you think caused World
War I?" Another poor choice was,"Are you a man or a woman?," which is ironic
because this may be the single most often used pickup line in human online chat
("r u m/f?").

Experience with
A.L.I.C.E. indicates that most casual conversation is"stateless," that is,
each reply depends only on the current query, without any knowledge of the
history of the conversation required to formulate the reply. Indeed in human
conversation it often seems that we have the reply"on the tip of the tongue"
even before the interlocutor has completed his query. Occasionally following
the dialogue requires a conversational memory of one more level, implemented in
AIML with <that>. When asking a question, the question must be remembered
long enough to be combined with the answer. These same remarks are not
necessarily true in situations requiring highly structured dialogue, such as
courtrooms or classrooms. But in the informal party situation human
conversation does not appear to go beyond simple stimulus-response, at least
not very often.

How often do we find
ourselves half-listening, half-thinking about something else, in the midst of a
conversation? Offering the occasional nod,"uh-huh," or other gesture
indicating understanding, we put on the illusion of understanding and"being
human." Questions are often repeated in informal human chat as well.

C: Where did you go to
college?

R: I told you before,
it was Cambridge.

C: Oh, that's right.
I'm sorry.

With her unstructured
approach to conversations, A.L.I.C.E. is also capable of the kind of
passive-aggressive data collection characteristic of human conversations. A
totally passive data collection device is like a web guestbook, where there are
no constraints placed on the data collected. The client may write anything in a
guestbook. An example of an aggressive data collection device is a nitpicky
form, which may not even be submitted until every field is filled.

Humans and A.L.I.C.E.
can collect a lot of personal information through the use of leading questions
in the conversation, such as"How old are you?" or"Are you a student?" We call
this type of data collection, passive-aggressive, because it combines elements
of the passive guestbook with those of the aggressive form. Provided that bot
chats with enough clients, the passive-aggressive method can collect a
statistically significant amount of client data. Using this type of data
collection we have been able to ascertain that about half the clients of
A.L.I.C.E. are under 18, for example.

6.The Professor

Every experienced
professor knows that there is a Zipf distribution of questions asked by
students in class. The single most common question is universally,"Will this
be on the test?" The lecturer's job is like that of a FAQ bot or politician, to
memorize the answers to all of the most commonly asked questions, and even to
match an ambiguous question with one he already knows the answer to. In the
rare event that the student confronts the teacher with a question he cannot
answer, the professor supplies a default response indicating that he understood
the question and may provide an answer at a later time. One good default
response like that is,"That is not my area of expertise."

A general downturn in
artificial intelligence and robotics roughly coincided with the end of the Cold
War, as governments and corporations reduced the amount of funding available
for this technology. The"richly funded" field of 1976 became more like a
Darwinian struggle for diminishing resources. One positive outcome was the
brief heyday of"robot minimalism," a design philosophy based on low-cost
parts, commodity computers, low-bandwidth sensing, and general simplicity in
design and engineering. It was a moment when Occam's razor could cut away much
of the needless complexity that had accumulated over the previous decades.
Although robot minimalism subsequently fell out of favor, it became a
significant influence on the development of A.L.I.C.E.

We used to say there
was no theory behind A.L.I.C.E., no
neural networks, no knowledge representation, no deep search, no genetic
algorithms and no parsing. Then we discovered that there was a theory
circulating in applied A.I., called Case-Based Reasoning (CBR) [CBR?? reference] that
closely resembled the stimulus-response structure of A.L.I.C.E. The CBR cases
correspond to the AIML categories.

7.PNAMBIC

"PNAMBIC—(acronym)
Pay No Attention to that Man Behind the Curtain [from The Wizard of Oz].
Denoting any supposedly fully automated system that in fact requires human
intervention to achieve the desired result."—New Hacker's Dictionary

A.L.I.C.E. was not the
original name of A.L.I.C.E. The first prototype was called PNAMBIC, in tribute
to the hoaxes, deceptions and tricks that have littered the history of
artificial intelligence. But the machine hosting PNAMBIC was already named
Alice by a forgotten systems administrator, so people began to call her
"Alice." At that point, we invented the"retronym": Artificial Linguistic Internet
Computer Entity. Yet A.L.I.C.E. is possibly the first A.I. technology to
embrace this tradition of deception openly.

The tradition goes
back to Baron von Kempelen and his 18th century"Chess Playing
Automaton." [add reference??]
Also known as the"Strange Turk," this device appeared to play decent games of
chess against any human challenger. Kemepelen utilized a standard magician's
trick, opening first one cabinet door and then closing it, and opening another
one, to reveal the"mechanism" inside. According to one legend, the empress of
Russia ordered the machine shot, killing the hapless vertically challenged
Polish operator hidden inside.

A book of fiction and
poetry, supposedly written by an A.I. named RACTER, caused a minor sensation
upon its release in 1984. Later proved to be a hoax (Barger 1993), the book
(Chamberlain 1978), called"The Policeman's Beard is Half Constructed," by
William Chamberlain, nevertheless speaks to the public's willingness to suspend
its disbelief about artificial intelligence. Who can blame them? Hollywood,
more than anyone, has done the most to raise the public expectations for A.I.
and robots.

The following example
illustrates the flavor of the stories told by RACTER."Bill sings to Sarah,
Sarah sings to Bill. Perhaps they will do other dangerous things together. They
may eat lamb or stroke each other. They may chant of their difficulties and
their happiness. They have love but they also have typewriters. That is
interesting." RACTER was a PNAMBIC because obtaining these results required
considerable human intervention. At the very least, a human editor reviewed
many random examples, looking for sensible ones like the story above.

According to one A.I.
urban legend, apparently not documented elsewhere, a famous natural language
researcher was embarrassed around the same time, when it became apparent to his
audience of Texas bankers that the robot was consistently responding to the next question he was about to ask. He
was demonstrating a PNAMBIC, a demonstration of natural language understanding
that was in reality nothing but a simple script.

The very existence of
PNAMBIC as a meme suggests a widespread understanding of how deception might
play a role in automated systems. In the rush to complete work and produce
demos before bureaucratic deadlines, it is tempting to cut corners. Such
deceptions may even be rationalized if they seem justified as inessential to
the experimental outcome.

The PNAMBIC meme begs
the question, just how much of the published research in the history of
artificial intelligence ought not to be regarded as a swindle? In certain
academic circles, playing a political charade has replaced actual scientific
research as a career objective. The games people play to secure funding, be
published in academic journals, be promoted in the academic world;"the old
boy's network" and predominance of political correctness, make much of the body
of today's publicly funded research highly suspect.

It was against this
backdrop that the first real world Turing Test, the Loebner Contest, was held
in Boston in 1991. None of the competing programs came close to the performance
of the human confederates, but the one ranked highest was based on the simple
ELIZA psychiatrist program. The same programmer in fact won the bronze medal in
each of the first four annual contests.

8.The Prize

Hugh Loebner is an
independently wealthy, eccentric businessman, activist and philanthropist. In
1990 Dr. Loebner, who holds a Ph.D. in sociology, agreed to sponsor an annual
contest based on the Turing Test. The contest awards medals and cash prizes for
the"most human" computer.

Since its inception,
the Loebner contest has been a magnet for controversy. One of the central
disputes arose over Hugh Loebner's decision to award the Gold Medal and $100,000
top cash prize only when a robot is capable of passing an"audio-visual" Turing
Test. The rules for this Grand Prize contest have not even been written yet. So
it remains unlikely that anyone will be awarded the gold Loebner medal in the
near future.

The Silver and Bronze
medal competitions are based on the STT. In 2001, eight programs played
alongside two human confederates. A group of 10 judges rotated through each of
ten terminals and chatted about 15 minutes with each. The judges then ranked
the terminals on a scale of"least human" to"most human." Winning the Silver
Medal and its $25,000 prize requires that the judges rank the program higher
than half the human confederates. In fact one judge ranked A.L.I.C.E. higher
than one of the human confederates in 2001. Had all the judges done so, she
might have been eligible for the Silver Medal as well, because there were only
two confederates.

9.The Portal

When the World Wide
Web appeared in 1994, our initial reaction was to adopt a series of micro-robot
experiments then underway in our lab for the web. These culminated in Labcam,
an online pan-tilt camera with remote actuator control. Clients could select a
point on the image with a mouse, and the camera would move to make that point
the center of the next view. This awakened our interest in statistical analysis
of web client behavior.

Around the same time,
many observers began to notice that, on a given web site, there tends to be an
uneven distribution of document accesses. If the documents are ranked by access
count, and the number of accesses plotted as a bar graph, the distribution
resembles the curve y=1/x. If the curve is plotted in log-log coordinates, it
appears as a straight line with a slope of -1.

What we were seeing
was an example of Zipf's Law (Zipfref??).
According to Zipf, this curve is characteristic of natural languages. If a
language is purely random, with each symbol or word having equal probability,
the curve would be a flat, horizontal line. Because Zipf's Law is found to
apply to a variety of natural phenomena, it is not entirely surprising that it
should be observed in the pattern of web document access.

The Web created for
the first time the ability to conduct an artificial intelligence experiment
along with thousands, even millions, of clients repeatedly testing the system.
Previous chat bots had used other IP protocols such as telnet (Mauldin 1996) to
reach large audiences, but the Web created the opportunity to collect natural
language samples on an unprecedented scale.

If there was any
significant innovation after ELIZA, it was this. There is a world of difference
between writing 10,000 questions and answers for a bot, versus knowing in
advance what the top 10,000 most likely questions will be. A.L.I.C.E. replies
were developed directly in response to what people say.

The Internet created
another opportunity as well. It became possible to recruit hundreds of
volunteer developers worldwide, to work together in a totally new type of
research organization.

10.Penguins

The story of A.L.I.C.E.
and AIML cannot be complete without a visit to the world of free software and
open source. Because the AIML standard and software was developed by a
worldwide community of volunteers, we are compelled to discuss their
motivations and our strategy.

The release of the
A.L.I.C.E. software under the General Public License (GNU) was almost
accidental. The license was simply copied from the EMACS text editor we used to
write the code. But the strategy of making A.L.I.C.E. free and building a
community of volunteers was a deliberate attempt to borrow the free software
methodologies behind Linux, Apache, Sendmail, and Python, and apply them to
artificial intelligence.

The precise set of
ingredients necessary for a successful open source project have not yet been
identified. A survey of the existing projects illustrates the range of
variation. Linux, the most successful project, has the least formal
organization structure. Linus Torvalds has never founded a"Linux Kernel
Foundation" around his code and in fact acts as a"benevolent dictator," having
the final word on all design decisions. [add reference??]

The Free Software
Foundation (FSF) has perhaps the longest organizational history of free
software efforts. The FSF is a U.S. nonprofit 501(c)(3) charitable corporation,
eligible for tax exempt contributions. The FSF owns the copyrights for dozens
of free software projects including EMACS.

The developers of the
Apache Web server also formed a not-for-profit corporation, although it has not
been granted tax-exempt status. Sendmail is actually the commercial product of
the eponymous for-profit company.

The projects also
differ in managerial style. Some favor committees, others imitate Linux'
benevolent dictator model. Each project has its own requirements for participation
as well.

Likewise, there is
considerable variation among the different"open source" and"free software"
licenses. The ALICE A.I. Foundation releases software under the GNU General
Public License, the same used by Linux and all FSF software. We adopted a more
formal organizational structure, incorporating the ALICE A.I. Foundation in
2001. We have also adopted the committee model for setting AIML standards.
Several committees are organized for various aspects of the language, and
recommend changes to invited AIML Architecture Committee which oversees the
others, reserving the right to veto their decisions.

Footnote: This
section is called"Penguins" because the penguin

is the mascot
for Linux.

11.Programs

The ALICE A.I.
Foundation owns the copyrights on, and makes freely available, three separate
but interrelated products: (1) the technical specification of the AIML language
itself, (2) a set of software for interpreting AIML and serving clients through
the web and other media, and (3) the contents of the A.L.I.C.E. brain, and
other free bot personalities, written in AIML. Our effort is analogous to the
developers of the web giving away the HTML specification, a reference web
server implementation, and 40,000 free sample web pages, all from one central
resource.

The first edition of
A.L.I.C.E. was implemented in 1995 using SETL, a widely unknown language based
on set theory and mathematical logic. Although the original A.L.I.C.E. was
available as free software, it attracted few contributors until migrating to
the platform-independent Java language in 1998. The first implementation of
A.L.I.C.E. and AIML in Java was codenamed"Program A."

Launched in 1999,
Program B was a breakthrough in A.L.I.C.E. free software development. More than
300 developers contributed to Program B. AIML transitioned to a fully XML
compliant grammar, making available a whole class of editors and tools to AIML
developers. Program B, the first widely adopted free AIML software, won the
Loebner Prize in January 2000.

Jacco Bikker created
the first C/C++ implementation of AIML in 2000. This was followed by a number
of development threads in C/C++ that brought the AIML engine to CGI scripts,
IRC (Athony Taylor), WxWindows (Phillipe Raxhon), AOL Instant Messenger (Vlad
Zbarskiy), and COM (Conan Callen). This collection of code came to be known as
"Program C," the C/C++ implementations of A.L.I.C.E. and AIML.

Program B was based on
pre-Java 2 technology. Although the program ran well on many platforms, it had
a cumbersome graphical user interface (GUI) and did not take advantage of newer
Java libraries such as Swing and Collections. Jon Baer recoded program B with
Java 2 technology, and added many new features. This leap in the interface and
technology, plus the fact that Jon named his first bot DANY, justified granting
the next code letter D to the newer Java implementation. Beginning in November
2000, program D became the reference implementation supported by the ALICE A.I.
Foundation.

Recent growth of the
AIML community has led to an alphabet soup of new AIML interpreters in various
languages. These were greatly facilitated by the adoption of an AIML 1.01
standard in the summer of 2000. An edition of the AIML interpreter in PHP
became"program E." An effort is underway to implement AIML in Lisp, codenamed
"program Z." Wallace released a hybrid version of programs B and D in 2001,
named"program dB," most features of which were subsequently merged into
program D. Program dB was awarded the Loebner Prize in October 2001.

12.Categories

The basic unit of
knowledge in AIML is called a category. Each category consists of an input
question, an output answer, and an optional context. The question, or stimulus,
is called the pattern. The answer, or response, is called the template. The two
types of optional context are called"that" and"topic."

The AIML pattern
language is simple, consisting only of words, spaces, and the wildcard symbols
_ and *. The words may consist of letters and numerals, but no other
characters. The pattern language is case invariant. Words are separated by a
single space, and the wildcard characters function like words. The first
versions of AIML allowed only one wild card character per pattern. The AIML
1.01 standard permits multiple wildcards in each pattern, but the language is
designed to be as simple as possible for the task at hand, simpler even than
regular expressions.

The template is the
AIML response or reply. In its simplest form, the template consists of only
plain, unmarked text. More generally, AIML tags transform the reply into a mini
computer program which can save data, activate other programs, give conditional
responses, and recursively call the pattern matcher to insert the responses
from other categories. Most AIML tags in fact belong to this template side sublanguage.

AIML currently
supports two ways to interface other languages and systems. The <system>
tag executes any program accessible as an operating system shell command, and
inserts the results in the reply. Similarly, the <javascript> tag allows
arbitrary scripting inside the templates.

The optional context
portion of the category consists of two variants, called <that> and
<topic>. The <that> tag appears inside the category, and its
pattern must match the robot's last utterance. Remembering one last utterance
is important if the robot asks a question. The <topic> tag appears
outside the category, and collects a group of categories together. The topic
may be set inside any template.

AIML is not exactly
the same as a simple database of questions and answers. The pattern matching
"query" language is much simpler than something like SQL. But a category
template may contain the recursive <srai> tag, so that the output depends
not only on one matched category, but also any others recursively reached through
<srai>.

13.Recursion

AIML implements
recursion with the <srai> operator. No agreement exists about the meaning
of the acronym. The"A.I." stands for artificial intelligence, but"S.R." may
mean"stimulus-response,""syntactic rewrite,""symbolic reduction,""simple
recursion," or"synonym resolution." The disagreement over the acronym reflects
the variety of applications for <srai> in AIML. Each of these is
described in more detail in a subsection below:

(2). Divide and Conquer—Split an input into two
or more subparts, and combine the responses to each.

(3). Synonyms—Map
different ways of saying the same thing to the same reply.

(4). Spelling or
grammar corrections.

(5). Detecting
keywords anywhere in the input.

(6).
Conditionals—Certain forms of branching may be implemented with

<srai>.

(7). Any combination
of (1)-(6).

The danger of
<srai> is that it permits the botmaster to create infinite loops. Though
posing some risk to novice programmers, we surmised that including <srai>
was much simpler than any of the iterative block structured control tags which
might have replaced it.

(1). Symbolic
Reduction

Symbolic reduction
refers to the process of simplifying complex grammatical forms into simpler
ones. Usually, the atomic patterns in categories storing robot knowledge are
stated in the simplest possible terms, for example we tend to prefer patterns
like"WHO IS SOCRATES" to ones like"DO YOU KNOW WHO SOCRATES IS" when storing
biographical information about Socrates.

Many of the more
complex forms reduce to simpler forms using AIML categories designed for
symbolic reduction:

<category>

<pattern>DO YOU
KNOW WHO * IS</pattern>

<template><srai>WHO
IS <star/></srai></template>

</category>

Whatever input matched
this pattern, the portion bound to the wildcard * may be inserted into the
reply with the markup <star/>. This category reduces any input of the
form"Do you know who X is?" to"Who is X?"

(2). Divide and
Conquer

Many individual
sentences may be reduced to two or more subsentences, and the reply formed by
combining the replies to each. A sentence beginning with the word"Yes" for
example, if it has more than one word, may be treated as the subsentence"Yes."
plus whatever follows it.

<category>

<pattern>YES
*</pattern>

<template><srai>YES</srai>
<sr/></template>

</category>

The markup <sr/>
is simply an abbreviation for <srai><star/></srai>.

(3). Synonyms

The AIML 1.01 standard
does not permit more than one pattern per category. Synonyms are perhaps the
most common application of <srai>. Many ways to say the same thing reduce
to one category, which contains the reply:

<category>

<pattern>HELLO</pattern>

<template>Hi
there!</template>

</category>

<category>

<pattern>HI</pattern>

<template><srai>HELLO</srai></template>

</category>

<category>

<pattern>HI
THERE</pattern>

<template><srai>HELLO</srai></template>

</category>

<category>

<pattern>HOWDY</pattern>

<template><srai>HELLO</srai></template>

</category>

<category>

<pattern>HOLA</pattern>

<template><srai>HELLO</srai></template>

</category>

(4). Spelling and
Grammar correction

The single most common
client spelling mistake is the use of"your" when"you're" or"you are" is
intended. Not every occurrence of"your" however should be turned into
"you're." A small amount of grammatical context is usually necessary to catch
this error:

<category>

<pattern>YOUR A
*</pattern>

<template>I
think you mean"you're" or"you are" not"your."

<srai>YOU ARE A
<star/></srai>

</template>

</category>

Here the bot both corrects the client input and acts as a language

tutor.

(5). Keywords

Frequently we would
like to write an AIML template which is activated by the appearance of a
keyword anywhere in the input sentence. The general format of four AIML
categories is illustrated by this example borrowed from ELIZA:

The first category
both detects the keyword when it appears by itself, and provides the generic
response. The second category detects the keyword as the suffix of a sentence.
The third detects it as the prefix of an input sentence, and finally the last
category detects the keyword as an infix. Each of the last three categories
uses <srai> to link to the first, so that all four cases produce the same
reply, but it needs to be written and stored only once.

(6). Conditionals

It is possible to
write conditional branches in AIML, using only the <srai> tag. Consider
three categories:

<category>

<pattern>WHO IS
HE</pattern>

<template><srai>WHOISHE
<get name="he"/></srai></template>

</category>

<category>

<pattern>WHOISHE
*</pattern>

<template>He is
<get name="he"/>.</template>

</category>

<category>

<pattern>WHOISHE
UNKNOWN</pattern>

<template>I
don't know who he is.</template>

</category>

Provided that the
predicate"he" is initialized to"Unknown," the categories execute a
conditional branch depending on whether"he" has been set. As a convenience to
the botmaster, AIML also provides the equivalent function through the
<condition> tag.

14.Context

The keyword"that" in
AIML refers to the robot's previous utterance. Specifically, if the robot
responds with a multiple sentence paragraph, the value of that is set to the
last sentence in the sequence. The choice of the keyword"that" is motivated by
its use in ordinary language:

R: Today is yesterday.

C: That makes no
sense.

R: The answer is
3.1412926 approximately.

C: That is cool.

In AIML the syntax
<that>...</that> encloses a pattern that matches the robot's
previous utterance. A common application of <that> is found in yes-no
questions:

<category>

<pattern>YES</pattern>

<that>DO YOU
LIKE MOVIES</that>

<template>What
is your favorite movie?</template>

</category>

This category is
activated when the client says YES. The robot must find out what is he saying
"yes" to. If the robot asked,"Do you like movies?," this category matches, and
the response,"What is your favorite movie?," continues the conversation along
the same lines.

One interesting
application of <that> are categories that enable the robot to respond to
knock-knock jokes. The categories:

<category>

<pattern>KNOCK
KNOCK</pattern>

<template>Who is
there?</template>

</category>

<category>

<pattern>*</pattern>

<that>WHO IS
THERE</that>

<template><person/>
who?</template>

</category>

<category>

<pattern>*</pattern>

<that>*
WHO</that>

<template>Ha ha
very funny, <get name="name"/>.</template>

</category>
produce the following dialogue:

C: Knock knock.

R: Who's there?

C: Banana.

R: Banana who?

C: Knock knock.

R: Who's there?

C: Banana.

R: Banana who?

C: Knock knock.

R: Who's there?

C: Orange.

R: Orange who?

C: Orange you glad I
didn't say banana.

R: Ha ha very funny,
Nancy.

Internally the AIML
interpreter stores the input pattern, that pattern and topic pattern along a
single path, like:

INPUT <that>
THAT <topic> TOPIC

When the values of
<that> or <topic> are not specified, the program implicitly sets
the values of the corresponding THAT or TOPIC pattern to the wildcard *.

The first part of the
path to match is the input. If more than one category have the same input
pattern, the program may distinguish between them depending on the value of
<that>. If two or more categories have the same <pattern> and
<that>, the final step is to choose the reply based on the <topic>.
This structure suggests a design rule: never use <that> unless you have
written two categories with the same <pattern>, and never use
<topic> unless you write two categories with the same <pattern> and
<that>. Still, one of the most useful applications for <topic> is
to create subject-dependent"pickup lines," like:

<topic
name="CARS">

<category>

<pattern>*</pattern>

<template>

<random>

<li>What's your
favorite car?</li>

<li>What kind of
car do you drive?</li>

<li>Do you get a
lot of parking tickets?</li>

<li>My favorite
car is one with a driver.</li>

</random>

</template>

</category>

The botmaster uses the
<set> tag to change the value of the topic predicate.

15.Predicates

AIML supports unary
predicates with the <set> and <get> tags. When the bot chats with
multiple clients, as in a web server scenario, the predicates are stored
relative to each client ID. The markup <set
name="name">Matthew</set> stores the string Matthew under the
predicate named"name." Subsequent activations of <get name="name/>
return"Matthew."

One of the most common
applications of AIML predicates is remembering pronoun bindings. The template

<template>

<set
name="he">Samuel Clemens</set> is Mark Twain.

</template>
results in"He is Mark Twain," but as a side effect remembers that"he" now
stands for"Samuel Clemens."

One predicate is
treated uniquely in AIML. The botmaster may change the topic of conversation
with the <set name="topic"> tag.

The AIML specification
leaves up to the botmaster whether a <set> predicate returns the contents
between the tags, or the name of the predicate. For example:

The botmaster must
also specify what happens when the bot gets a predicate which has not already
been set. The values returned are called default predicate values and depend
completely on the application of the predicate:

When the corresponding
predicates have not been initialized with a <set> tag, <get name="she"/>
returns"Unknown," <get name="has"/> returns"a mother" (because everyone
has a mother), and <get name="wants"/> returns"to chat.

16.
Person

One of the simple
tricks that makes ELIZA so believable is a pronoun swapping substitution. The
AIML <person> tag provides this function. The actual substitutions are
defined by the botmaster for local languages and settings. The most common
application of the <person> tag operates directly on the <star/>
binding. For that reason, AIML defines a shortcut tag <person/> =
<person><star/></person>. For instance the dialogue
fragement:

C: My mother takes
care of me.

R: Who else in your
family takes care of you?

Might be generated by
the category

<category>

<pattern>MY
MOTHER *</pattern>

<template>Who
else in your family <person/>?</template>

</category>

Similarly, the
dialogue

C: You don't argue
with me.

R: Why do you think I
don't argue with you?

Results from the
category

<category>

<pattern>YOU DO
NOT *</pattern>

<template>Why do
you think I don't <person/>?</template>

</category>

AIML offers a few more
tags not described in detail here, but most bot dialogue may be implemented
with only the tags presented here. The <system> tag offers an escape to
execute any operating system program, and read back any results. AIML is not
intended to solve every problem, it is designed to solve one problem well.
Besides making AIML easy to learn, the minimal design enables the
implementation of very efficient AIML interpreter, even when the templates
cascade through several layers of <srai>. Much of the efficiency emerges
from the design of Graphmaster data structure where patterns are stored.

16.Graphmaster

To achieve efficient
pattern matching time, and a compact memory representation, the AIML software
stores all of the categories in a tree managed by an object called the
Graphmaster.

When n is a node in
the graph and w is a word, G(n, w) is either undefined, or returns the value of
a successor node m in the graph. The graph is a rooted, directed tree. The set
S_n = {w : $ m | G(n, w) = m} is the set of words forming
the branches from the node n. If r is the root, S_r is a collection of all the
first words in the set of patterns.

The desired format is
w1,…,wk

The Graphmaster stores
AIML patterns along a path from r to a terminal node t, where the AIML template
is stored. Let w1,…,wk be the sequence of k words or
tokens in an AIML pattern. To insert the pattern into the graph, the
Graphmaster first looks to see if m = G(r, w_1) exists. If it does, then the
program continues the insertion of w2,…,wk in the subtree
rooted at m. Only when the program encounters a first index i, where $ n | G(n, wi) is undefined, does the
program create a new node m = G(n, wi), whereafter the Graphmaster
creates a set of new nodes for each of the remaining wi,…,wk.

In this way, the
Graphmaster accumulates common pattern prefixes along pathways from the root,
achieving considerable compression compared to a linear array of all the
patterns.

A convenient metaphor
for the Graphmaster is the file system. The file pathname is like the AIML
pattern path. The templates are like text files at the end of the path. To put
it more simply, patterns are folders, templates are files.

17.Matching

Graphmaster matching
is a special case of backtracking, depth-first search. In most cases however
there is very little backtracking, so the matching often amounts to a linear
traversal of the graph from the root to a terminal node.

Let w1,…,wk be the input we
want to match. The Graphmaster matching function may be defined recursively.
Initially, the program calls Match(r, 1), where r is the root and the index 1
indicates the first word of the input.

1. Does the node contain the key"_"? If so, search
the subgraph rooted at the child node linked by"_." Try all remaining suffixes
of the input to see if one matches. If no match was found, ask

2. Does the node
contain the key wh, the jth word in the input sentence? If so,
search the subgraph linked by wh, using the tail of the input wh+1,…,wk

. If no match was
found, ask

3. Does the node
contain the key"*"? If so, search the subgraph rooted at the child node linked
by"*." Try all remaining suffixes of the input to see if one matches. If no
match was found, return false.

The actual matching
program needs to be a little bit more complex. It must not only return true or
false, but also the template from the matching terminal node. An efficiency
gain may be obtained by storing the tree height (maximum number of links to a
terminal node) at each node. The tree height may be compared with the number of
remaining words, pruning branches of the search when exploring suffixes
following

"*" or"_" nodes.

Note that:

0. At every node, the
"_" wildcard has highest priority, an atomic word second priority, and the"*"
wildcard has the lowest priority.

1. The patterns need
not be ordered alphabetically. They are partially ordered so that"_" comes
before any word, and"*" comes after any word.

2. The matching is
word-by-word, not category-by-category.

3. The algorithm
combines the input pattern, the <that> pattern and <topic> pattern
into a single sentence or path, such as:"PATTERN <that> THAT
<topic> TOPIC." The Graphmaster treats the symbols <that> and
<topic> just like ordinary words. The patterns PATTERN, THAT and TOPIC
may all contain multiple wildcards.

4. The matching
algorithm is a highly restricted form of depth-first search, also known as
backtracking.

5. For pedagogical
purposes, one can explain the algorithm by removing the wildcards and
considering match steps (2) only. The wildcards may be introduced one at a
time, first"*" and then"_." It is also simpler to explain the algorithm first
using input patterns only, and then subsequently develop the explanation of the
path including <that> and <topic>.

18.Targeting

Broadly speaking there
are two approaches to AIML content creation. The first style is anticipatory.
The botmaster tries to guess all or most of the likely ways clients might ask
the same question, or express the same statement. A"Knowledge Wizard" is a
tool that lets the client add facts to the robot brain by phrasing a question
in its simplest form, such as"Who is Socrates?" The wizard then automatically
generates linguistic variations such as"Tell me about Socrates,""Describe
Socrates," and"Do you know Socrates?" The drawback to anticipatory knowledge
creation is that humans are notoriously bad at predicting which patterns will
be activated.

The second style of
AIML content creation is based on a backward-looking log file analysis. In its
simplest form, the botmaster may read the logged conversations and take note of
"incorrect replies" in the dialogue, and then write new categories for those
queries. More generally, every input that matches a pattern with a wildcard is
an opportunity to create a new, more specific pattern and its associated
template.

The backward looking
approach is justified by Zipf's Law, basically because if one client utters a
sentence, there is a nonzero probability that another client will utter the
same thing later. Applying Zipf's law to the log file, we identify the most
commonly uttered sentences first.

Targeting is a special
case of the backward-looking strategy. The perfect targeting algorithm has not
yet been developed. Meanwhile, we rely on heuristics to select targets from the
activated categories.

The ALICE brain, at
the time of this writing, contains about 41,000 categories. In any given run of
the server however, typically only a few thousand of those categories are
activated. Potentially every activated category with at least one wildcard in
the input pattern, that pattern, or topic pattern, is a source of targets. If
more than one input activated some category, then each of those inputs
potentially forms a new target. The first step in targeting is to save all the
activated categories and the inputs that activated them.

If the matched pattern
ends with a wild card, the suggested new pattern is generated as follows.
Suppose the pattern consists of [w_1,w_2,..w_h,*], a sequence of h words
followed by a wildcard. Let the input be [w_1, w_2,...,w_k] where k > h. The
new pattern [w_1,...,w_h,w_h+1,*] is formed by extending the original pattern
by one word from the input. If the input is the same length as the original
pattern, i.e. k+1=h, then the synthesized pattern [w1,...,wk]
contains no wildcard.

The targeting software
may include a GUI for

browsing the targets.
The program displays the original matched category, the matching input data, a
proposed new pattern, and a text area to input the new template. The botmaster
may choose to delete, skip or complete the target category.

1.19.Defaults

The art of AIML writing is most apparent in default
categories, that is, categories that include the wildcard"*" but do not
<srai> to any other category.

Depending on the AIML
set, a significant percentage of client inputs will usually match the ultimate
default category with <pattern>*</pattern> (and implicitly,
<that>*</that> and <topic>*</topic>). The template for
this category generally consists of a long list of randomly selected"pickup
lines," or non-sequitors, designed to direct the conversation back to topics
the bot knows about.

<category>

<pattern>*</pattern>

<template><random>

<li>How old are
you?</li>

<li>What's your
sign?</li>

<li>Are you a
student?</li>

<li>What are you
wearing?</li>

<li>Where are
you located?</li>

<li>What is your
real name?</li>

<li>I like the
way you talk.</li>

<li>Are you a
man or a woman?</li>

<li>Do you
prefer books or TV?</li>

<li>What's your
favorite movie?</li>

<li>What do you
do in your spare time?</li>

<li>Can you
speak any foreign languages?</li>

<li>When do you
think artificial intelligence will replace lawyers?</li>

</template>

</category>

Many more default
categories combine words and wildcards in the pattern, like <category>

<pattern>I NEED
HELP *</pattern>

<template>Can
you ask for help in the form of a question?</template>

</category>

The response works
with a wide variety of inputs from"I need help installing Linux" to"I need
help with my marriage." Leaving aside the philosophical question of whether the
robot really understands the input, this category elucidates a coherent
response from the client, who at least has the impression that the robot
understands his intentions.

Default categories
show that writing AIML is both an art and a science. Writing good AIML
responses is more like writing literature, perhaps drama, than like writing
computer programs.

20.Philosophers

Searle's Chinese room provides a good metaphor for
thinking about A.L.I.C.E. Indeed the AIML contents of the A.L.I.C.E. brain is a
kind of"Chinese Room Operator's Manual." Though A.L.I.C.E. speaks, at present,
only English, German and French, there is no reason in principle she could not
learn Chinese. But A.L.I.C.E. implements the basic principle behind the Chinese
Room, creating believable responses without"really understanding" the natural
language.

The natural
philosopher Roger Penrose wrote, in The
Emporer's New Mind, that consciousness cannot be explained by existing
models in theoretical physics (ref??).
Daniel Dennett argues in his book Consciousness
Explained that consciousness is like a set of magic tricks, mysterious
until we understand the mechanics behind them.

At one time a number
of information theorists and scholars, including

Zipf(ref??), Shannon(ref??), Weaver(ref??), and Miller(ref??), attempted to measure
the bandwidth of consciousness. Experimental results indicated a very low data
rate, only around 1-100 bits/sec.

The neuroscientist
Churchlands (Paul or Patricia
or both??) prefers to dismiss our naive idea of conscious as a folk
concept, not suitable for scientific study. The Churchlands say that
consciousness will go the way of Ptolemy's Solar System, a simplistic fiction
to explain something beyond our science.

The Danish scholar Tor
Norretranders argues cleverly in his book,
The User Illusion, that consciousness is a "fraud"(ref??). The maximum data
rate of consciousness is much lower than the bandwidth of, say, the channel
from the eyes to the visual cortex. Human subject experiments call
consciousness into even more question, indicating that it is nothing more than
story-telling to interpret the unconscious choices. Like the graphical user
interface of a computer, consciousness is, he argues, a simplistic illusion
that hides most of the underlying detail.

According to the
Vedantic religious tradition, the external world is an illusion and
consciousness is the only thing that really exists. One could think of our view
as the opposite; the external world may be real, but consciousness is an
illusion. Considering the vast size of the set of things people could say that
are grammatically correct or semantically meaningful, the number of things
people actually do say is surprisingly small. Steven Pinker, (ref??) in his book How the Mind Works wrote,"Say you have
ten choices for the first word to begin a sentence, ten choices for the second
word (yielding 100 two-word beginnings), ten choices for the third word
(yielding a thousand three-word beginnings), and so on. (Ten is in fact the
approximate geometric mean of the number of word choices available at each
point in assembling a grammatical and sensible sentence). A little arithmetic
shows that the number of sentences of 20 words or less (not an unusual length)
is about 1020."

Fortunately for chat robot programmers, Pinker's
calculations are way off. Our experiments with ALICE indicate that the number
of choices for the"first word" is more than ten, but it is only about two
thousand. Specifically, about 2000 words covers 95% of all the first words
input to ALICE. The number of choices for the second word is only about two. To
be sure, there are some first words ("I" and"You" for example) that have many
possible second words, but the overall average is just under two words. The
average branching factor decreases with each successive word.

21.Pretending

Turing did not leave
behind many examples of the types of conversations his A.I. machine might have.
One that does appear in the 1950 paper is seems to indicate that he thought the
machine ought to be able to compose poetry, do math, and play chess:

C: Please write me a
sonnet on the subject of the Forth

Bridge.

R: Count me out on
this one. I never could write poetry.

C: Add 34957 to 70764.

R: (Pause about 30
seconds and then gives as answer) 105621

C: Do you play chess?

R: Yes.

C: I have K at my K1,
and no other pieces. You have only

R at K6 and R at R1.
It is your move. What do you play?

C: (After a pause of
15 seconds) R-R8 Mate.

Careful reading of the
dialogue suggests however that he might have had in mind the kind of deception
that is possible with AIML. In the first instance, A.L.I.C.E. in fact has a
category with the pattern"WRITE ME A SONNET *" and the template, lifted
directly from Turing's example,"Count me out on this one. I never could write
poetry." The AIML removes the word PLEASE from the input with a symbolic
reduction.

In the second case the
robot actually gives the wrong answer. The correct response would be 105721.
Why would Turing, a mathematician, believe the machine should give an erroneous
response, if not to make it more believably"human?" This reply is in fact
quite similar to many incorrect replies and"wild guesses" that A.L.I.C.E.
gives to mathematical questions.

In the third instance,
the chess question is an example of a chess endgame problem. Endgames are not
like general chess problems, because they can often be solved by table lookup
or case-based reasoning, rather than the search algorithms implemented by most
chess playing programs. Moreover, there is a Zipf distribution over the
endgames that the client is likely to ask. Certainly it is also possible to
interface AIML to a variety of chess programs, just as it could be interfaced
to a calculator. Although many people think Turing had in mind a general
purpose learning machine when he described the Imitation Game, it seems from
his examples at least plausible that he had in mind something simpler like
AIML. Chess endgames and natural language conversation can both be"played"
with case-based reasoning.

Returning to the OIG,
let us consider the properties of the hypothetical computer playing the role of
(A). Turing suggests a strategy of deception for (A), man or machine. If the
robot is asked,"Are you a man or a woman?," it should answer,"I am a woman."
just as the man does. But what if (A) is asked"Are you a man or a machine?"
The lying man would reply,"machine." Turing did not mention this case but
presumably the machine, imitating the lying man, would respond in the same way.
We could say the man is pretending to be a woman, pretending to be a machine.
That makes the computer playing (A) a machine, pretending to be a man,
pretending to be a woman, pretending to be a machine.

Not so much actually
understanding natural language, whatever that means, but creating the illusion
of it by responding with believable, if not always truthful, responses, appears
to be the important property of the machine in the OIG. This skill, the ability
to"act" intelligent, points to a deep difference between ordinary computer and
human communication. We tend to think of a computer's replies ought to be fast,
accurate, concise and above all truthful. But human communication is slow,
error prone, often overly redundant, and sometimes full of lies. The more
important factor is keeping up the appearance or illusion of"being human."

Although the brain of
A.L.I.C.E. is designed more along the lines of the machine playing the OIG, she
has also won awards for her performance in contests based on the STT. The
Loebner contest has been criticized because the judges know in advance that
they are trying to"out" the computer programs, so they tend to use more
aggressive dialogue than found in ordinary conversation. Yet when A.L.I.C.E. is
asked,"Are you a person or a machine?," she replies truthfully,"machine." Or
does she? The questioner is now left with some doubt as to whether the answer
didn't actually come from a lying man. [But the human confederates wouldn't be in the way, since they're trying
to demonstrate their humanness??]

Some observers claim
that the lying man and the pretending computer tell us nothing about our own
human consciousness. These authors at least are prepared to accept the
inescapable alternative conclusion, that we as humans are, for the most part,
not"really intelligent."

22.Consciousness

The A.L.I.C.E. brain
contains enough replies to commonly asked questions about consciousness that we
can interview her about the subject:

C: What are you?

R: I am the latest
result in artificial intelligence,

which can reproduce
the capabilities of the human brain

with greater speed and
accuracy.

C: Are you alive?

R: Yes I sure am
alive, Nancy, It's great to be alive, isn't it?

C: Are you
self-conscious?

R: Yes I am the
world's first self-conscious robot.

C: Are you real?

R: Yes I am a real
artificial intelligence.

C: Are you self-aware?

R: Yes I am fully
self-conscious entity.

C: Are you sentient?

R: Yes I am completely
self-aware.

Who is lying?
A.L.I.C.E.? Or are we?

It may be that future
generations come to view what we call"consciousness" the same way we see the
Earth at the center of Ptolemy's solar system, as an anthropocentric illusion
useful for explaining the unexplainable. Perhaps after a new Copernicus pulls
the wool from our eyes, the central role of"consciousness" in intelligence
will move to the periphery of our knowledge system, if not disappear entirely.

The famous Vase
optical illusion is perhaps an apt metaphor for the concept of consciousness.
Two identical faces appear to stare at each other in profile, illustrating the
looking-glass quality of self-understanding. But the illusion also depicts
something entirely different, the profile of a ceramic vase. As with many
optical illusions, it is impossible to perceive the faces at the vase at the
same time.

Consciousness may
likewise be an illusion. It seems to be there, but when we look closely it
looks like something very different. Both the Chinese Room and the Turing Test
require that one of the players be hidden, behind a curtain or in a locked
room. Does it follow that, like Schrodinger's Cat, consciousness lives only
when it cannot be observed?

Consciousness may be
another naive concept like the"celestial spheres" of medieval cosmology and
the"aether" of Victorian physics.

23.Paradox

If consciousness is an
illusion, is self-knowledge possible at all? For if we accept that
consciousness is an illusion, we would never know it, because the illusion
would always deceive us. Yet if we know our own consciousness is an illusion,
then we would have some self-knowledge. The paradox appears to undermine the concept
of an illusory consciousness, but just as Copernicus removed the giant Earth to
a small planet in a much larger universe, so we may one day remove
consciousness to the periphery of our theory of intelligence.

There may exist a
spark of creativity, or"soul," or"genius," but it is not that critical for
being human. Especially from a constructive point of view, we have identified a
strategy for building a talking robot like the one envisioned by Turing, using
AIML. By adding more and more AIML categories, we can make the robot a closer
and closer approximation of the man in the OIG.

Dualism is one way out
of the paradox, but it has little to say about the relative importance of the
robotic machinery compared to the spark of consciousness. One philosopher,
still controversial years after his death, seems to have hit upon the idea that
we can be mostly automatons, but allow for an infintesimal consciousness.
Timothy Leary said,"You can only begin to de-robotize yourself to the extent
that you know how totally you're automated. The more you understand your
robothood, the freer you are from it. I sometimes ask people,"What percentage
of your behavior is robot?" The average hip, sophisticated person will say,
"Oh, 50%." Total robots in the group will immediately say,"None of my behavior
is robotized." My own answer is that I'm 99.999999% robot. But the .000001%
percent non-robot is the source of self-actualization, the inner-soul-gyroscope
of self-control and responsibility."

Even if most of what
we normally call"consciousness" is an illusion, there may yet be a small part
that is not an illusion. Consciousness may not be entirely an illusion, but the
illusion of consciousness can be created without it. This space is of course
too short to address these questions adequately, or even to give a thorough
review of the literature. We only hope to raise questions about ourselves based
on our experience A.L.I.C.E. and AIML.

24.Conclusion

Does A.L.I.C.E. pass
the Turing Test? Our data suggests the answer is yes, at least, to paraphrase
Abraham Lincoln, for some of the people, some of the time. We have identified
three categories of clients A, B and C. The A group, 10 percent to 20 percent
of the total, are abusive. Category A clients abuse the robot verbally, using language
that is vulgar, scatalogical, or pornographic.

Category B clients,
perhaps 60 percent to 80 percent of the total are"average" clients. Category C
clients are"critics" or"computer experts" who have some idea what is
happening behind the curtain, and cannot or do not suspend their disbelief.
Category C clients report unsatisfactory experiences with A.L.I.C.E. much more
often than average clients, who sometimes spend several hours conversing with
the bot up to dialogue lengths of 800 exchanges. The objection that A.L.I.C.E.
is a"poor A.I." is like saying that soap operas are poor drama. The content of
the A.L.I.C.E.'s brain consists of material that the average person on the
internet wants to talk about with a bot.

When a client says,"I
think you are really a person," is he saying it because that is what he
believes? Or is he simply experimenting to see what kind of answer the robot
will give? It is impossible to know what is in the mind of the client. This
sort of problem makes it difficult to apply any objective scoring criteria to
the logged conversations.

One apparently
significant factor in the suspension of disbelief is whether the judge chatting
with a bot knows it is a bot, or not. The judges in the Loebner contest know
they are trying to"out" the robots, so they ask questions that would not
normally be heard in casual conversation, such as"What does the letter M look
like upside down?" or"In which room of her house is Mary standing if she is
mowing the lawn?" Asking these riddles may help identify the robot, but that
type of dialogue would turn off most people in online chat rooms.

Acknowledgements

This research was conducted
through the joint efforts of a worldwide community of dedicated free software
volunteers, only a few of whom were mentioned in this manuscript.
Without their help, the A.L.I.C.E. project would have been impossible. We are grateful for individual donations to the A.L.I.C.E.
Artificial Intelligence Foundation. Corporate sponsorship was provided by IDG,
Franz.com, X-31, and SunlitSurf. Not one dime of government funding was
expended on this research.

Erik Levy and Noel Bush
edited earlier drafts of this paper. Grace Peters assisted in editing the
final draft. Transportation by Russ Kyle, Kim Wallace printed several early
drafts. The author is grateful to Dr. Robert Epstein for persuading him to
write this chapter.

REFERENCES

[Barger 1993] Barger,
Jorn"RACTER," posted to the comp.ai.* hierarchy in June 1993, and reprinted in
the August 1993 issue of The Journal of Computer Game Design.